RectBivariateSpline — SciPy v1.15.2 Manual (original) (raw)
scipy.interpolate.
class scipy.interpolate.RectBivariateSpline(x, y, z, bbox=[None, None, None, None], kx=3, ky=3, s=0)[source]#
Bivariate spline approximation over a rectangular mesh.
Can be used for both smoothing and interpolating data.
Parameters:
x,yarray_like
1-D arrays of coordinates in strictly ascending order. Evaluated points outside the data range will be extrapolated.
zarray_like
2-D array of data with shape (x.size,y.size).
bboxarray_like, optional
Sequence of length 4 specifying the boundary of the rectangular approximation domain, which means the start and end spline knots of each dimension are set by these values. By default,bbox=[min(x), max(x), min(y), max(y)].
kx, kyints, optional
Degrees of the bivariate spline. Default is 3.
sfloat, optional
Positive smoothing factor defined for estimation condition:sum((z[i]-f(x[i], y[i]))**2, axis=0) <= s where f is a spline function. Default is s=0, which is for interpolation.
Notes
If the input data is such that input dimensions have incommensurate units and differ by many orders of magnitude, the interpolant may have numerical artifacts. Consider rescaling the data before interpolating.
Methods
| __call__(x, y[, dx, dy, grid]) | Evaluate the spline or its derivatives at given positions. |
|---|---|
| ev(xi, yi[, dx, dy]) | Evaluate the spline at points |
| get_coeffs() | Return spline coefficients. |
| get_knots() | Return a tuple (tx,ty) where tx,ty contain knots positions of the spline with respect to x-, y-variable, respectively. |
| get_residual() | Return weighted sum of squared residuals of the spline approximation: sum ((w[i]*(z[i]-s(x[i],y[i])))**2,axis=0) |
| integral(xa, xb, ya, yb) | Evaluate the integral of the spline over area [xa,xb] x [ya,yb]. |
| partial_derivative(dx, dy) | Construct a new spline representing a partial derivative of this spline. |